LAUSR

Abstract Subglacial melt has important implications for ice-sheet dynamics. Locating and identifying subglacial lakes are expensive and time-consuming, requiring radar surveys or satellite methods. We explore three methods to identify source regions for lakes using seven continent-wide environmental characteristics that are sensitive to or influenced by ice-sheet temperature. A simple comparison of environmental properties at lake locations with their continent-wide distributions suggests a statistical relationship (high Kolmogorov-Smirnov statistic) between stable lake locations and ice thickness and surface temperatures, indicating melting under passive conditions. Active lakes, in contrast, show little correlation with direct thermally influenced parameters, instead exhibiting large statistical differences with horizontal velocity and bedrock elevation. More sophisticated techniques, including principal component analysis (PCA) and machine learning (ML) classification, provide better spatial identification of lake types. Positive PCA scores derived from the environmental characteristics correlate with stable lakes, whereas negative values correspond to active lakes. ML methods can also identify regions where subglacial lake melt sources are probable. While ML provides the most accurate classification maps, the combination of approaches adds deeper knowledge of the primary controls on lake formation and the environmental settings in which they are likely to be found.